DECIMER.ai:一个开放的平台,用于科学出版物中的光学化学结构自动识别,细分和识别
Kohulan Rajan1, Henning Otto Brinkhaus1, M Isabel Agea2
1Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessingstr. 8, 07743, Jena, Germany.
Nature communications
|August 19, 2023
概括
DECIMER.ai使用深度学习自动识别科学文献中的化学结构. 这种开源工具通过将图像转换成机器可读的格式来提高研究人员的数据可访问性.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 科学中的人工智能.
背景情况:
- 在科学出版物中,大量的化学信息被锁定在非机器可读格式中.
- 从文献中手动提取化学结构是耗时且容易出现错误的.
- 自动化化学结构识别对于推进数据挖掘和知识发现至关重要.
研究的目的:
- 开发一个开源平台,DECIMER.ai,用于化学结构描述的自动细分,分类和翻译.
- 从印刷文献中提供可访问的用于光学化学结构识别 (OCSR) 的工具.
- 提高公共数据库中化学信息的机器可读性.
主要方法:
- 利用深度学习,计算机视觉和自然语言处理技术.
- 开发用于细分和分类化学结构图像的专门工具.
- 使用光学化学结构识别 (OCSR) 核心应用程序进行高性能识别.
主要成果:
- DECIMER.ai在化学结构识别的基准数据集上表现出色.
- 分段和分类工具是独特的,公开可用的软件包.
- 源代码,训练模型和数据集都是在允许许可证下发布的.
结论:
- DECIMER.ai从科学文献中显著推进了自动化化学结构识别.
- 该平台提高了化学数据的可访问性和机器可读性.
- 开源可用性促进了化学信息学研究的更广泛采用和进一步发展.
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